Can you Afford Risking a Delayed Regulatory Submission?

Before the FDA start reviewing a submission, your submission must first overcome the new hurdle of ensuring that it conforms to the standards required in the FDA Data Standards Catalog.

“The FDA may refuse to file for New Drug Applications (NDAs) and Biologics License Applications (BLAs) or refuse to receive for Abbreviated NDAs (ANDAs) any electronic submission whose study data do not conform to the required standards specified in the FDA Data Standards Catalog.” - FDA

In a recent analysis conducted by the FDA, 32% of submissions with study data had critical data conformance issues.

When a submission is rejected for lack of study data conformance, the submission sequence is not even transferred from the FDA Electronic Submission Gateway into the FDA electronic document rooms where the FDA Review process officially starts!

Then only 50% of new NME/IND applications that passed the screening process for study data conformance are approved on first submissions by the FDA. There is a median delay of 435 days to approval following the first unsuccessful submission and 24% of the first-time submission failures include inconsistent study results either across endpoints or sites and studies.

Re-submission of failed applications is costly, delaying marketing approval as the process needs to start again with increased timelines, and delaying the availability of vital new drugs to patients.

The efficacy associated reasons for failed first-time submissions are:

Poor efficacy when compared with the standard of care

Uncertainties related to dose selection

Choice of study endpoints that fail to adequately reflect a clinically meaningful effect

Inconsistent results with different end points selected

Study conduct due to missing data and/or data integrity issues

Choice of study endpoints that fail to adequately reflect a clinically meaningful effect

How to reduce the risk of a delay?

We review your submission content to:

Ensure that data are formatted and submitted according to the correct data standards
Uncover atypical data patterns within or across studies that act as a red flag warning to investigate further for an FDA Reviewer
Investigate the influence of atypical data patterns on the robustness of the submission efficacy and safety conclusions

Submit an RFI

Study Data Conformance

Quanticate offers CDISC Compliance services and CDISC mapping expertise across multiple situations. When reviewing submission data our team will ensure that CDISC standards are met before the submission is sent with support on:

SDTM Compliant Datasets and supporting documentation (define.xml, cSDRG, aCRF)

ADaM Compliant Datasets and supporting documentation (define.xml, ADRG)

Integrated summaries

eCTD Compliance

Quanticate are vigilant in keeping up-to-date with the latest CDISC, regulatory and other news, such as PhUSE guidance to ensure that you have the latest accepted standards applied and comply with the FDA Data Standards Catalog.

Data Quality Oversight

Quanticate provide a Data Quality Oversight (DQO) service to examine the quality and fitness of the data package associated with the submission. Quanticate has selected the industry leading FDA endorsed software of CluePoints to perform its DQO analysis. We use CluePoints’ machine learning functionality to detect spurious data patterns both within and across studies. If appropriate, we also use software such SAS JMP for further statistical investigation. DQO enables the quick identification of outliers and inconsistencies across studies which can then be addressed to improve data quality.

CluePoints Logo_for_web file-246825434

Statistical Review of Submission Data

Our biostatisticians review the impact of anomalies and missing data on the results of the submission in an attempt to pre-empt the regulatory reviewer’s likely comments so that they can be addressed in the initial submission.

In addition, we can broaden the review to:

Review of the distribution and balance of the study populations across studies, sites and regions
Confirm populations are as defined in protocol
Analyze the consistency of efficacy analyses across studies, sites, regions, populations and timepoints
Review dosing evidence used to justify the dosing decision
Examine appropriateness of endpoints, by reviewing existing regulatory guidance and relevant literature
Understand confounding factors, for example use of concomitant medication
Review distributions of adverse events, including, for example, severity, seriousness and death across studies, sites, regions and populations
Check for dose-related toxicity effects
Determine whether correct interpretations have been made along with robustness of conclusions